Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2018, Article ID 4028473, 6 pages
https://doi.org/10.1155/2018/4028473
Research Article

The Prediction of Drug-Disease Correlation Based on Gene Expression Data

1School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
2Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
3Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
4College of Food Science and Technology, Shanghai Ocean University, No. 999 Hu Cheng Huan Road, Shanghai 201306, China
5IPQ Analytics, LLC/Strategic Medicine, Philadelphia, PA, USA
6Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 30007, China

Correspondence should be addressed to Ying Yu; nc.ca.sbis@gniyuy and Lu Xie; moc.kooltuo@7102xeixul

Received 11 November 2017; Revised 18 January 2018; Accepted 11 February 2018; Published 25 March 2018

Academic Editor: Jialiang Yang

Copyright © 2018 Hui Cui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. A. Curtin, J. Fridlyand, T. Kageshita et al., “Distinct sets of genetic alterations in melanoma,” The New England Journal of Medicine, vol. 353, no. 20, pp. 2135–2147, 2005. View at Publisher · View at Google Scholar · View at Scopus
  2. Z. Sheng, Y. Sun, Z. Yin, K. Tang, and Z. Cao, “Advances in computational approaches in identifying synergistic drug combinations,” Briefings in Bioinformatics, 2017. View at Publisher · View at Google Scholar
  3. J. Jia, X. Ma, Z. W. Cao, Y. X. Li, and Y. Z. Chen, “Erratum: Mechanisms of drug combinations: Interaction and network perspectives (Nature Reviews Drug Discovery (2009) vol. 8 (111-128) 10.1038/nrd2683),” Nature Reviews Drug Discovery, vol. 8, no. 6, p. 516, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Yang, H. Tang, Y. Li et al., “DIGRE: drug-induced genomic residual effect model for successful prediction of multidrug effects,” CPT: Pharmacometrics & Systems Pharmacology, vol. 4, no. 2, pp. 91–97, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. P. B. Chapman et al., “Improved survival with vemurafenib in melanoma with BRAF V600E mutation,” The New England Journal of Medicine, vol. 364, no. 26, pp. 2507–16, 2011. View at Google Scholar
  6. H. S. Nelson, “Advair: Combination treatment with fluticasone propionate/salmeterol in the treatment of asthma,” The Journal of Allergy and Clinical Immunology, vol. 107, no. 2, pp. 397–416, 2001. View at Publisher · View at Google Scholar · View at Scopus
  7. Z. Wu, X. Zhao, and L. Chen, “A systems biology approach to identify effective cocktail drugs,” BMC Systems Biology, vol. 4, no. Suppl 2, p. S7, 2010. View at Publisher · View at Google Scholar
  8. M. A. Held, C. G. Langdon, J. T. Platt et al., “Genotype-selective combination therapies for melanoma identified by high-throughput drug screening,” Cancer Discovery, vol. 3, no. 1, pp. 52–67, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. Q. Xu, Y. Xiong, H. Dai et al., “PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm,” Journal of Theoretical Biology, vol. 417, pp. 1–7, 2017. View at Publisher · View at Google Scholar · View at Scopus
  10. X. Zhao, M. Iskar, G. Zeller, M. Kuhn, V. van Noort, and P. Bork, “Prediction of drug combinations by integrating molecular and pharmacological data,” PLoS Computational Biology, vol. 7, no. 12, Article ID e1002323, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. G. Jin, H. Zhao, X. Zhou, and S. T. C. Wong, “An enhanced Petri-Net model to predict synergistic effects of pairwise drug combinations from gene microarray data,” Bioinformatics, vol. 27, no. 13, pp. i310–i316, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. D. S. Wishart, C. Knox, A. C. Guo et al., “DrugBank: a comprehensive resource for in silico drug discovery and exploration,” Nucleic Acids Research, vol. 34, pp. D668–D672, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Liu, Q. Wei, G. Yu, W. Gai, Y. Li, and X. Chen, “DCDB 2.0: a major update of the drug combination database,” Database, vol. 2014, Article ID bau124, 2014. View at Publisher · View at Google Scholar
  14. N. Borisov et al., “A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency,” Cell Cycle, pp. 1–6, 2017. View at Publisher · View at Google Scholar
  15. Y. Sun, Z. Sheng, C. Ma et al., “Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer,” Nature Communications, vol. 6, article 9481, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Reuter, D. V. Spacek, and M. Snyder, “High-throughput sequencing technologies,” Molecular Cell, vol. 58, no. 4, pp. 586–597, 2015. View at Publisher · View at Google Scholar